A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation - Saat, Parisa; Nogovitsyn, Nikita; Hassan, Muhammad Yusuf; Ganaie, Muhammad Athar; Souza, Roberto; Hemmati, Hadi (Frontiers in Neuroinformatics, 2022)
Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more robust to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline models, and a benchmark for assessing different brain MR image segmentation techniques. Our work currently supports two segmentation tasks: skull-stripping and white-matter, gray-matter, and cerebrospinal fluid segmentation, but it is readily extensible to other brain structures.